3 citations · 6 across the 16 of their papers we have counts for
9 papers · 1 filter
RUBRIC-ARROW: Alternating Pointwise Rubric Reward Modeling for LLM Post-training in Non-verifiable Domains
Haoxiang Jiang, Zihan Dong, Tianci Liu +5
Pointwise reward modeling offers critical signals for LLM post-training, yet struggles with absolute scoring in subjective, non-verifiable settings. Rubric-based methods address th…
PEANuT: Parameter-Efficient Adaptation with Weight-aware Neural Tweakers
Yibo Zhong, Haoxiang Jiang, Lincan Li +5
Fine-tuning large pre-trained foundation models often yields excellent downstream performance but is prohibitively expensive when updating all parameters. Parameter-efficient fine-…
Counterfactual Fairness by Combining Factual and Counterfactual Predictions
Zeyu Zhou, Tianci Liu, Ruqi Bai +3
In high-stake domains such as healthcare and hiring, the role of machine learning (ML) in decision-making raises significant fairness concerns. This work focuses on Counterfactual…
LIDAO: Towards Limited Interventions for Debiasing (Large) Language Models
Tianci Liu, Haoyu Wang, Shiyang Wang +2
Large language models (LLMs) have achieved impressive performance on various natural language generation tasks. Nonetheless, they suffer from generating negative and harmful conten…
Towards Poisoning Fair Representations
Tianci Liu, Haoyu Wang, Feijie Wu +4
Fair machine learning seeks to mitigate model prediction bias against certain demographic subgroups such as elder and female. Recently, fair representation learning (FRL) trained b…
SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification
Tianci Liu, Haoyu Wang, Yaqing Wang +3
Recent years have witnessed increasing concerns towards unfair decisions made by machine learning algorithms. To improve fairness in model decisions, various fairness notions have…
Optimization for Amortized Inverse Problems
Tianci Liu, Tong Yang, Quan Zhang +1
Incorporating a deep generative model as the prior distribution in inverse problems has established substantial success in reconstructing images from corrupted observations. Notwit…
Density Regression and Uncertainty Quantification with Bayesian Deep Noise Neural Networks
Daiwei Zhang, Tianci Liu, Jian Kang
Deep neural network (DNN) models have achieved state-of-the-art predictive accuracy in a wide range of supervised learning applications. However, accurately quantifying the uncerta…
An Empirical Comparison of GANs and Normalizing Flows for Density Estimation
Tianci Liu, Jeffrey Regier
Generative adversarial networks (GANs) and normalizing flows are both approaches to density estimation that use deep neural networks to transform samples from an uninformative prio…